Load data and prepared fpkm values

datasets = as.data.frame(scan("Stanford_datasets.txt",list(setname="",seqBatch="",species="",tissue=""),sep="\t"))
fpkmMat <- as.matrix(read.table('Stanford_datasets_fpkmMat.txt',header=FALSE,sep='\t'))

Log transform data to normalize

logTransformed_fpkmMat = log2(fpkmMat+1)
colnames(logTransformed_fpkmMat) <- datasets$setname

Plot correlation heatmaps using different methods

Apply PCA method to uncorrected data

transposeLogTransformed_fpkmMat = t(logTransformed_fpkmMat)
pca_proc <- prcomp(transposeLogTransformed_fpkmMat[,apply(transposeLogTransformed_fpkmMat, 2, var, na.rm=TRUE) != 0],scale=TRUE,center=TRUE)

Check PCA statistics

summary(pca_proc)
## Importance of components:
##                            PC1     PC2     PC3      PC4      PC5      PC6
## Standard deviation     54.2999 43.5061 39.3992 35.81511 31.77971 25.86337
## Proportion of Variance  0.2012  0.1292  0.1059  0.08755  0.06893  0.04565
## Cumulative Proportion   0.2012  0.3304  0.4364  0.52391  0.59284  0.63849
##                             PC7      PC8      PC9    PC10     PC11     PC12
## Standard deviation     24.77243 22.33937 21.69784 21.1730 19.82838 18.28041
## Proportion of Variance  0.04188  0.03406  0.03213  0.0306  0.02683  0.02281
## Cumulative Proportion   0.68037  0.71443  0.74656  0.7772  0.80399  0.82680
##                            PC13     PC14     PC15     PC16     PC17     PC18
## Standard deviation     17.41969 17.00162 16.05240 15.65210 15.16221 14.56541
## Proportion of Variance  0.02071  0.01973  0.01759  0.01672  0.01569  0.01448
## Cumulative Proportion   0.84751  0.86724  0.88483  0.90155  0.91724  0.93172
##                            PC19     PC20     PC21     PC22     PC23     PC24
## Standard deviation     13.08324 12.96681 12.29599 12.07214 11.38150 11.18347
## Proportion of Variance  0.01168  0.01148  0.01032  0.00995  0.00884  0.00854
## Cumulative Proportion   0.94340  0.95487  0.96519  0.97514  0.98398  0.99252
##                            PC25      PC26
## Standard deviation     10.47129 5.633e-14
## Proportion of Variance  0.00748 0.000e+00
## Cumulative Proportion   1.00000 1.000e+00

Transfer PCA data to plots

plotData = datasets[,c("setname","species","tissue")]
plotData$PC1 <- pca_proc$x[,1]
plotData$PC2 <- pca_proc$x[,2]
plotData$PC3 <- pca_proc$x[,3]

Plot the first and the second principal components

Plot the first and the second principal components with centroids

plotData_pca <- prcomp(pca_proc$x[, -1])
fviz_pca_ind(plotData_pca,
             geom.ind = "point", # show points only (nbut not "text")
             col.ind = plotData$species, # color by groups
             addEllipses = TRUE, # Concentration ellipses
             legend.title = "Species",
             labs = "PCA before correction"
             )

Plot the first, the second and the third principal components

Test for significance of correlations between the matched tissues PC values of human and mouse

cor.test(plotData$PC1[1:13],plotData$PC1[14:26],method="pearson") 
## 
##  Pearson's product-moment correlation
## 
## data:  plotData$PC1[1:13] and plotData$PC1[14:26]
## t = 1.4318, df = 11, p-value = 0.18
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.1978558  0.7775285
## sample estimates:
##       cor 
## 0.3963364
cor.test(plotData$PC2[1:13],plotData$PC2[14:26],method="pearson") 
## 
##  Pearson's product-moment correlation
## 
## data:  plotData$PC2[1:13] and plotData$PC2[14:26]
## t = 4.6926, df = 11, p-value = 0.000658
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.4829263 0.9432117
## sample estimates:
##       cor 
## 0.8166208
cor.test(plotData$PC3[1:13],plotData$PC3[14:26],method="pearson") 
## 
##  Pearson's product-moment correlation
## 
## data:  plotData$PC3[1:13] and plotData$PC3[14:26]
## t = 0.8382, df = 11, p-value = 0.4198
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.3537165  0.7013257
## sample estimates:
##       cor 
## 0.2450217